Quantum computing offers a transformative approach to artificial neural networks (ANNs) through quantum quantization, which enhances vector search efficiency by converting float32 vectors into qbit vectors and leveraging quantum entanglement. This conversion facilitates the use of Grover’s algorithm, a quantum search method that locates a marked item in an unsorted database in O(√N) time, a notable improvement over classical algorithms that operate in O(N) time. An innovative technique called transposition further optimizes Grover’s algorithm by reducing the number of iterations from O(√N) to O(√D), where D is the vector space dimension, thus enabling vector searches in constant time. This advancement signifies a significant leap in handling arbitrary-sized databases efficiently, harnessing the unique properties of quantum systems to create highly effective vector search algorithms.